Overview

Dataset statistics

Number of variables15
Number of observations20631
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 MiB
Average record size in memory120.0 B

Variable types

Numeric14
Categorical1

Alerts

(Total temperature at LPC outlet) (â—¦R) is highly overall correlated with (Total temperature at HPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Total temperature at HPC outlet) (â—¦R) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Total temperature at LPT outlet) (â—¦R) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Total pressure at HPC outlet) (psia) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Physical fan speed) (rpm) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Physical core speed) (rpm) is highly overall correlated with (Corrected core speed) (rpm)High correlation
(Static pressure at HPC outlet) (psia) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Ratio of fuel flow to Ps30) (pps/psia) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Corrected fan speed) (rpm) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Corrected core speed) (rpm) is highly overall correlated with (Physical core speed) (rpm)High correlation
(Bypass Ratio) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Bleed Enthalpy) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(HPT coolant bleed (lbm/s)) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(LPT coolant bleed (lbm/s)) is highly overall correlated with (Total temperature at LPC outlet) (â—¦R) and 10 other fieldsHigh correlation
(Total pressure in bypass-duct) (psia) is highly imbalanced (86.0%)Imbalance

Reproduction

Analysis started2023-04-09 23:33:59.147129
Analysis finished2023-04-09 23:34:18.303517
Duration19.16 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct310
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44305227
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:18.368532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21385542
Q10.33584337
median0.43072289
Q30.53915663
95-th percentile0.71385542
Maximum1
Range1
Interquartile range (IQR)0.20331325

Descriptive statistics

Standard deviation0.15061845
Coefficient of variation (CV)0.3399564
Kurtosis-0.11204294
Mean0.44305227
Median Absolute Deviation (MAD)0.10240964
Skewness0.31652589
Sum9140.6114
Variance0.022685919
MonotonicityNot monotonic
2023-04-09T19:34:18.461553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3885542169 190
 
0.9%
0.406626506 189
 
0.9%
0.3975903614 188
 
0.9%
0.4186746988 184
 
0.9%
0.4397590361 179
 
0.9%
0.3704819277 175
 
0.8%
0.4277108434 175
 
0.8%
0.4096385542 172
 
0.8%
0.4307228916 168
 
0.8%
0.4578313253 167
 
0.8%
Other values (300) 18844
91.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01204819277 2
< 0.1%
0.01807228916 3
< 0.1%
0.02710843373 4
< 0.1%
0.03012048193 1
 
< 0.1%
0.03313253012 2
< 0.1%
0.03614457831 2
< 0.1%
0.03915662651 1
 
< 0.1%
0.0421686747 1
 
< 0.1%
0.04518072289 2
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%
0.9909638554 1
< 0.1%
0.9819277108 1
< 0.1%
0.9728915663 1
< 0.1%
0.9578313253 1
< 0.1%
0.9518072289 1
< 0.1%
0.9457831325 1
< 0.1%
0.9427710843 1
< 0.1%
0.9337349398 1
< 0.1%
0.9307228916 2
< 0.1%
Distinct3012
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42474643
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:18.552574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21953346
Q10.33180728
median0.41552213
Q30.5088293
95-th percentile0.66339656
Maximum1
Range1
Interquartile range (IQR)0.17702202

Descriptive statistics

Standard deviation0.1336636
Coefficient of variation (CV)0.31469036
Kurtosis0.0077618224
Mean0.42474643
Median Absolute Deviation (MAD)0.088293002
Skewness0.30894581
Sum8762.9435
Variance0.017865959
MonotonicityNot monotonic
2023-04-09T19:34:18.645111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4155221278 27
 
0.1%
0.4081098757 26
 
0.1%
0.4129060388 25
 
0.1%
0.4593416176 25
 
0.1%
0.3666884674 24
 
0.1%
0.3032483104 23
 
0.1%
0.4251144539 23
 
0.1%
0.3932853717 23
 
0.1%
0.4011336385 23
 
0.1%
0.3658164378 22
 
0.1%
Other values (3002) 20390
98.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.0004360148245 1
< 0.1%
0.01744059298 1
< 0.1%
0.02071070416 1
< 0.1%
0.02834096359 1
< 0.1%
0.02964900807 1
< 0.1%
0.03095705254 1
< 0.1%
0.0355352082 1
< 0.1%
0.03749727491 1
< 0.1%
0.04229343798 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9568345324 1
< 0.1%
0.9522563767 1
< 0.1%
0.9282755614 1
< 0.1%
0.9210813168 1
< 0.1%
0.9121430129 1
< 0.1%
0.9066928276 1
< 0.1%
0.8953564421 1
< 0.1%
0.8912143013 1
< 0.1%
0.8835840419 1
< 0.1%
Distinct4051
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45043521
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:18.737359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2256921
Q10.33946658
median0.43534774
Q30.54532411
95-th percentile0.73295071
Maximum1
Range1
Interquartile range (IQR)0.20585753

Descriptive statistics

Standard deviation0.15193458
Coefficient of variation (CV)0.33730619
Kurtosis-0.16368086
Mean0.45043521
Median Absolute Deviation (MAD)0.10195814
Skewness0.44319434
Sum9292.9288
Variance0.023084118
MonotonicityNot monotonic
2023-04-09T19:34:18.829382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4517218096 20
 
0.1%
0.3750844024 18
 
0.1%
0.4203241053 18
 
0.1%
0.4181296421 18
 
0.1%
0.5364618501 18
 
0.1%
0.4775489534 18
 
0.1%
0.3541525996 17
 
0.1%
0.4208305199 16
 
0.1%
0.478055368 16
 
0.1%
0.3210668467 16
 
0.1%
Other values (4041) 20456
99.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.0496286293 1
< 0.1%
0.05908170155 1
< 0.1%
0.06819716408 1
< 0.1%
0.07056043214 1
< 0.1%
0.07494935854 1
< 0.1%
0.08288318704 1
< 0.1%
0.08625928427 1
< 0.1%
0.08659689399 1
< 0.1%
0.08862255233 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.95729237 1
< 0.1%
0.9496961512 1
< 0.1%
0.9480081026 1
< 0.1%
0.9448008103 1
< 0.1%
0.9437879811 1
< 0.1%
0.9427751519 1
< 0.1%
0.9407494936 1
< 0.1%
0.939061445 1
< 0.1%
0.9378798109 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.3 KiB
1.0
20225 
0.0
 
406

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters61893
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 20225
98.0%
0.0 406
 
2.0%

Length

2023-04-09T19:34:18.906400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-09T19:34:18.972414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 20225
98.0%
0.0 406
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 21037
34.0%
. 20631
33.3%
1 20225
32.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41262
66.7%
Other Punctuation 20631
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21037
51.0%
1 20225
49.0%
Other Punctuation
ValueCountFrequency (%)
. 20631
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61893
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21037
34.0%
. 20631
33.3%
1 20225
32.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21037
34.0%
. 20631
33.3%
1 20225
32.7%
Distinct513
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56645913
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:19.044430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30434783
Q10.47665056
median0.57809984
Q30.66988728
95-th percentile0.77938808
Maximum1
Range1
Interquartile range (IQR)0.19323671

Descriptive statistics

Standard deviation0.14252693
Coefficient of variation (CV)0.25161027
Kurtosis-0.15794922
Mean0.56645913
Median Absolute Deviation (MAD)0.096618357
Skewness-0.39432894
Sum11686.618
Variance0.020313927
MonotonicityNot monotonic
2023-04-09T19:34:19.135451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6070853462 116
 
0.6%
0.6296296296 115
 
0.6%
0.6231884058 110
 
0.5%
0.6586151369 110
 
0.5%
0.576489533 108
 
0.5%
0.6264090177 107
 
0.5%
0.6280193237 106
 
0.5%
0.6682769726 105
 
0.5%
0.652173913 104
 
0.5%
0.5909822866 103
 
0.5%
Other values (503) 19547
94.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.07890499195 1
< 0.1%
0.08051529791 1
< 0.1%
0.09178743961 1
< 0.1%
0.09339774557 1
< 0.1%
0.1014492754 2
< 0.1%
0.1030595813 1
< 0.1%
0.1046698873 1
< 0.1%
0.1062801932 2
< 0.1%
0.1078904992 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9677938808 1
< 0.1%
0.9452495974 1
< 0.1%
0.9420289855 1
< 0.1%
0.9371980676 1
< 0.1%
0.9355877617 1
< 0.1%
0.9323671498 1
< 0.1%
0.9275362319 1
< 0.1%
0.9259259259 1
< 0.1%
0.922705314 1
< 0.1%
Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29795703
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:19.227472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13636364
Q10.22727273
median0.28787879
Q30.36363636
95-th percentile0.48484848
Maximum1
Range1
Interquartile range (IQR)0.13636364

Descriptive statistics

Standard deviation0.10755376
Coefficient of variation (CV)0.36097069
Kurtosis0.33314901
Mean0.29795703
Median Absolute Deviation (MAD)0.075757576
Skewness0.47941086
Sum6147.1515
Variance0.01156781
MonotonicityNot monotonic
2023-04-09T19:34:19.316492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3181818182 1181
 
5.7%
0.303030303 1159
 
5.6%
0.2878787879 1149
 
5.6%
0.2727272727 1126
 
5.5%
0.2575757576 1077
 
5.2%
0.3333333333 1069
 
5.2%
0.2424242424 1050
 
5.1%
0.3484848485 1033
 
5.0%
0.2272727273 1013
 
4.9%
0.2121212121 910
 
4.4%
Other values (43) 9864
47.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01515151515 3
 
< 0.1%
0.0303030303 9
 
< 0.1%
0.04545454545 16
 
0.1%
0.06060606061 33
 
0.2%
0.07575757576 72
 
0.3%
0.09090909091 145
 
0.7%
0.1060606061 201
1.0%
0.1212121212 339
1.6%
0.1363636364 426
2.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9393939394 1
 
< 0.1%
0.9090909091 1
 
< 0.1%
0.8484848485 1
 
< 0.1%
0.8181818182 2
 
< 0.1%
0.7121212121 1
 
< 0.1%
0.696969697 1
 
< 0.1%
0.6818181818 2
 
< 0.1%
0.6666666667 13
0.1%
0.6515151515 13
0.1%
Distinct6403
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19524787
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:19.406512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.093421879
Q10.14076102
median0.17468366
Q30.21399085
95-th percentile0.39598851
Maximum1
Range1
Interquartile range (IQR)0.07322983

Descriptive statistics

Standard deviation0.099088574
Coefficient of variation (CV)0.50750143
Kurtosis9.3786813
Mean0.19524787
Median Absolute Deviation (MAD)0.036480302
Skewness2.5553649
Sum4028.1588
Variance0.0098185454
MonotonicityNot monotonic
2023-04-09T19:34:19.497533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666965808 16
 
0.1%
0.1733823925 15
 
0.1%
0.1741900745 15
 
0.1%
0.1576325945 15
 
0.1%
0.1861706901 15
 
0.1%
0.1756259535 15
 
0.1%
0.1472224715 14
 
0.1%
0.1764336355 14
 
0.1%
0.1625235574 14
 
0.1%
0.1962667145 14
 
0.1%
Other values (6393) 20484
99.3%
ValueCountFrequency (%)
0 1
< 0.1%
0.009512698555 1
< 0.1%
0.01139728978 1
< 0.1%
0.01207035807 1
< 0.1%
0.01566005564 1
< 0.1%
0.01597415418 1
< 0.1%
0.01951898053 1
< 0.1%
0.0199228215 1
< 0.1%
0.02001256394 1
< 0.1%
0.02212151126 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9783272009 1
< 0.1%
0.9279368213 1
< 0.1%
0.9192766759 1
< 0.1%
0.9115139549 1
< 0.1%
0.9099883335 1
< 0.1%
0.9056358252 1
< 0.1%
0.8955398008 1
< 0.1%
0.8936103383 1
< 0.1%
0.8888091178 1
< 0.1%
Distinct159
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41140961
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:19.589553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.17857143
Q10.29761905
median0.39285714
Q30.50595238
95-th percentile0.71130952
Maximum1
Range1
Interquartile range (IQR)0.20833333

Descriptive statistics

Standard deviation0.15898059
Coefficient of variation (CV)0.38642898
Kurtosis-0.17219188
Mean0.41140961
Median Absolute Deviation (MAD)0.10714286
Skewness0.46932909
Sum8487.7917
Variance0.025274829
MonotonicityNot monotonic
2023-04-09T19:34:19.681574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3630952381 341
 
1.7%
0.4285714286 338
 
1.6%
0.380952381 332
 
1.6%
0.3571428571 332
 
1.6%
0.369047619 331
 
1.6%
0.3988095238 326
 
1.6%
0.3095238095 321
 
1.6%
0.375 319
 
1.5%
0.3511904762 318
 
1.5%
0.3452380952 311
 
1.5%
Other values (149) 17362
84.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.005952380952 3
< 0.1%
0.01785714286 2
 
< 0.1%
0.02380952381 1
 
< 0.1%
0.02976190476 1
 
< 0.1%
0.03571428571 1
 
< 0.1%
0.04166666667 3
< 0.1%
0.04761904762 3
< 0.1%
0.05357142857 6
< 0.1%
0.05952380952 6
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.994047619 1
 
< 0.1%
0.9702380952 1
 
< 0.1%
0.9404761905 1
 
< 0.1%
0.9285714286 4
< 0.1%
0.9226190476 4
< 0.1%
0.9166666667 3
< 0.1%
0.9107142857 1
 
< 0.1%
0.9047619048 2
 
< 0.1%
0.8928571429 5
< 0.1%
Distinct427
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58069723
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:19.773594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28784648
Q10.48400853
median0.59488273
Q30.69509595
95-th percentile0.81236674
Maximum1
Range1
Interquartile range (IQR)0.21108742

Descriptive statistics

Standard deviation0.15726085
Coefficient of variation (CV)0.27081385
Kurtosis-0.14491657
Mean0.58069723
Median Absolute Deviation (MAD)0.10660981
Skewness-0.44240724
Sum11980.365
Variance0.024730975
MonotonicityNot monotonic
2023-04-09T19:34:19.864684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6268656716 143
 
0.7%
0.5820895522 136
 
0.7%
0.5671641791 131
 
0.6%
0.6119402985 129
 
0.6%
0.6332622601 126
 
0.6%
0.6076759062 125
 
0.6%
0.6396588486 124
 
0.6%
0.5991471215 123
 
0.6%
0.5906183369 121
 
0.6%
0.5842217484 121
 
0.6%
Other values (417) 19352
93.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.02985074627 2
< 0.1%
0.05330490405 1
< 0.1%
0.05543710021 1
< 0.1%
0.0618336887 1
< 0.1%
0.06396588486 1
< 0.1%
0.06823027719 1
< 0.1%
0.07036247335 1
< 0.1%
0.07249466951 1
< 0.1%
0.078891258 2
< 0.1%
ValueCountFrequency (%)
1 2
< 0.1%
0.9936034115 1
< 0.1%
0.9850746269 1
< 0.1%
0.9765458422 1
< 0.1%
0.9744136461 2
< 0.1%
0.9722814499 1
< 0.1%
0.9701492537 1
< 0.1%
0.9680170576 1
< 0.1%
0.9637526652 1
< 0.1%
0.9616204691 1
< 0.1%
Distinct56
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31787116
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:19.955755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.16176471
Q10.23529412
median0.30882353
Q30.38235294
95-th percentile0.51470588
Maximum1
Range1
Interquartile range (IQR)0.14705882

Descriptive statistics

Standard deviation0.10576311
Coefficient of variation (CV)0.3327232
Kurtosis0.38724376
Mean0.31787116
Median Absolute Deviation (MAD)0.073529412
Skewness0.46979242
Sum6558
Variance0.011185836
MonotonicityNot monotonic
2023-04-09T19:34:20.044848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3235294118 1164
 
5.6%
0.3088235294 1144
 
5.5%
0.2941176471 1129
 
5.5%
0.3382352941 1127
 
5.5%
0.2794117647 1112
 
5.4%
0.3529411765 1099
 
5.3%
0.2647058824 1005
 
4.9%
0.25 987
 
4.8%
0.3676470588 976
 
4.7%
0.2352941176 952
 
4.6%
Other values (46) 9936
48.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01470588235 1
 
< 0.1%
0.02941176471 1
 
< 0.1%
0.04411764706 2
 
< 0.1%
0.05882352941 12
 
0.1%
0.07352941176 19
 
0.1%
0.08823529412 54
 
0.3%
0.1029411765 95
0.5%
0.1176470588 170
0.8%
0.1323529412 219
1.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9852941176 1
 
< 0.1%
0.9705882353 1
 
< 0.1%
0.8970588235 1
 
< 0.1%
0.8235294118 1
 
< 0.1%
0.75 2
 
< 0.1%
0.7205882353 3
 
< 0.1%
0.7058823529 6
< 0.1%
0.6911764706 7
< 0.1%
0.6764705882 8
< 0.1%
Distinct6078
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22609517
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:20.135882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11644649
Q10.17187016
median0.20951595
Q30.24961296
95-th percentile0.42039942
Maximum1
Range1
Interquartile range (IQR)0.077742801

Descriptive statistics

Standard deviation0.09844244
Coefficient of variation (CV)0.43540267
Kurtosis8.8546645
Mean0.22609517
Median Absolute Deviation (MAD)0.038910104
Skewness2.3725536
Sum4664.5695
Variance0.0096909139
MonotonicityNot monotonic
2023-04-09T19:34:20.582352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2010011353 17
 
0.1%
0.2162761895 17
 
0.1%
0.1906801528 16
 
0.1%
0.2108060687 15
 
0.1%
0.2100836 15
 
0.1%
0.2092579214 15
 
0.1%
0.2084322427 15
 
0.1%
0.2113221179 15
 
0.1%
0.1896480545 15
 
0.1%
0.2117349572 15
 
0.1%
Other values (6068) 20476
99.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.007998761482 1
< 0.1%
0.01486221488 1
< 0.1%
0.01718443596 1
< 0.1%
0.0197646816 1
< 0.1%
0.02084838477 1
< 0.1%
0.02332542058 1
< 0.1%
0.02497677779 1
< 0.1%
0.02518319744 1
< 0.1%
0.02724739395 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9820930953 1
< 0.1%
0.9788935907 1
< 0.1%
0.9718237176 1
< 0.1%
0.9420992879 1
< 0.1%
0.9284755909 1
< 0.1%
0.9281143565 1
< 0.1%
0.9095881928 1
< 0.1%
0.9015894313 1
< 0.1%
0.8938486944 1
< 0.1%

(Bypass Ratio)
Real number (ℝ)

Distinct1918
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45111805
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:20.675373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.23470566
Q10.34628703
median0.43863024
Q30.54136206
95-th percentile0.71604463
Maximum1
Range1
Interquartile range (IQR)0.19507503

Descriptive statistics

Standard deviation0.14430565
Coefficient of variation (CV)0.31988445
Kurtosis-0.12143
Mean0.45111805
Median Absolute Deviation (MAD)0.096960369
Skewness0.38825858
Sum9307.0165
Variance0.02082412
MonotonicityNot monotonic
2023-04-09T19:34:20.766393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4078491728 38
 
0.2%
0.4113120431 37
 
0.2%
0.4690265487 36
 
0.2%
0.459022701 35
 
0.2%
0.3382070027 34
 
0.2%
0.4632550981 32
 
0.2%
0.4605617545 32
 
0.2%
0.4317045017 31
 
0.2%
0.3693728357 31
 
0.2%
0.375913813 31
 
0.2%
Other values (1908) 20294
98.4%
ValueCountFrequency (%)
0 1
< 0.1%
0.01154290112 1
< 0.1%
0.02077722201 1
< 0.1%
0.04193920739 2
< 0.1%
0.04463255098 1
< 0.1%
0.05309734513 1
< 0.1%
0.05809926895 1
< 0.1%
0.06156213928 1
< 0.1%
0.06848787995 1
< 0.1%
0.06887264332 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9953828396 1
< 0.1%
0.934590227 1
< 0.1%
0.9318968834 1
< 0.1%
0.9307425933 1
< 0.1%
0.9295883032 1
< 0.1%
0.9253559061 1
< 0.1%
0.9230473259 1
< 0.1%
0.9222777992 1
< 0.1%
0.9203539823 1
< 0.1%

(Bleed Enthalpy)
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43422116
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:20.844410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.33333333
median0.41666667
Q30.5
95-th percentile0.66666667
Maximum1
Range1
Interquartile range (IQR)0.16666667

Descriptive statistics

Standard deviation0.12906359
Coefficient of variation (CV)0.29723007
Kurtosis-0.039174043
Mean0.43422116
Median Absolute Deviation (MAD)0.083333333
Skewness0.35312566
Sum8958.4167
Variance0.016657409
MonotonicityNot monotonic
2023-04-09T19:34:20.907424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.4166666667 5445
26.4%
0.3333333333 4578
22.2%
0.5 4063
19.7%
0.5833333333 2339
11.3%
0.25 2022
 
9.8%
0.6666666667 1185
 
5.7%
0.1666666667 452
 
2.2%
0.75 436
 
2.1%
0.8333333333 72
 
0.3%
0.08333333333 30
 
0.1%
Other values (3) 9
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.08333333333 30
 
0.1%
0.1666666667 452
 
2.2%
0.25 2022
 
9.8%
0.3333333333 4578
22.2%
0.4166666667 5445
26.4%
0.5 4063
19.7%
0.5833333333 2339
11.3%
0.6666666667 1185
 
5.7%
0.75 436
 
2.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.9166666667 7
 
< 0.1%
0.8333333333 72
 
0.3%
0.75 436
 
2.1%
0.6666666667 1185
 
5.7%
0.5833333333 2339
11.3%
0.5 4063
19.7%
0.4166666667 5445
26.4%
0.3333333333 4578
22.2%
0.25 2022
 
9.8%
Distinct120
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52424082
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:20.987443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27131783
Q10.43410853
median0.53488372
Q30.62790698
95-th percentile0.73643411
Maximum1
Range1
Interquartile range (IQR)0.19379845

Descriptive statistics

Standard deviation0.14011351
Coefficient of variation (CV)0.26726936
Kurtosis-0.11282911
Mean0.52424082
Median Absolute Deviation (MAD)0.093023256
Skewness-0.3584452
Sum10815.612
Variance0.019631796
MonotonicityNot monotonic
2023-04-09T19:34:21.079464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5581395349 485
 
2.4%
0.5813953488 476
 
2.3%
0.5271317829 472
 
2.3%
0.5658914729 460
 
2.2%
0.5503875969 458
 
2.2%
0.5348837209 457
 
2.2%
0.5426356589 455
 
2.2%
0.5736434109 452
 
2.2%
0.519379845 447
 
2.2%
0.511627907 447
 
2.2%
Other values (110) 16022
77.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.01550387597 1
 
< 0.1%
0.03100775194 1
 
< 0.1%
0.03875968992 1
 
< 0.1%
0.04651162791 1
 
< 0.1%
0.05426356589 1
 
< 0.1%
0.06201550388 3
 
< 0.1%
0.06976744186 5
< 0.1%
0.07751937984 7
< 0.1%
0.08527131783 9
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.984496124 1
 
< 0.1%
0.9302325581 1
 
< 0.1%
0.9147286822 1
 
< 0.1%
0.9069767442 2
 
< 0.1%
0.8992248062 2
 
< 0.1%
0.8914728682 3
 
< 0.1%
0.8837209302 1
 
< 0.1%
0.8759689922 10
< 0.1%
0.8682170543 7
< 0.1%
Distinct4745
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54612726
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.3 KiB
2023-04-09T19:34:21.172484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.27513118
Q10.45236123
median0.5574427
Q30.65258216
95-th percentile0.77230047
Maximum1
Range1
Interquartile range (IQR)0.20022093

Descriptive statistics

Standard deviation0.14947649
Coefficient of variation (CV)0.27370267
Kurtosis-0.11703945
Mean0.54612726
Median Absolute Deviation (MAD)0.099972383
Skewness-0.35037496
Sum11267.151
Variance0.022343221
MonotonicityNot monotonic
2023-04-09T19:34:21.263506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5909969622 23
 
0.1%
0.5643468655 17
 
0.1%
0.545981773 16
 
0.1%
0.6604529136 16
 
0.1%
0.6583816625 15
 
0.1%
0.6281413974 15
 
0.1%
0.6289698978 15
 
0.1%
0.6038387186 15
 
0.1%
0.5606186136 15
 
0.1%
0.6030102182 15
 
0.1%
Other values (4735) 20469
99.2%
ValueCountFrequency (%)
0 1
< 0.1%
0.01781275891 1
< 0.1%
0.02485501243 1
< 0.1%
0.05012427506 1
< 0.1%
0.05399061033 1
< 0.1%
0.05454294394 1
< 0.1%
0.0582711958 1
< 0.1%
0.06268986468 2
< 0.1%
0.06296603148 1
< 0.1%
0.06351836509 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9921292461 1
< 0.1%
0.9834299917 1
< 0.1%
0.975283071 1
< 0.1%
0.9722452361 1
< 0.1%
0.9594034797 1
< 0.1%
0.9555371444 2
< 0.1%
0.9549848108 1
< 0.1%
0.9504280585 1
< 0.1%
0.9457332229 1
< 0.1%

Interactions

2023-04-09T19:34:16.810179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:33:59.814295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.001755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.229666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.449523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.653447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.829711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.258578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.460847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.655116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.844383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.017648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.462973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.625912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.896200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:33:59.901158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.087775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.316688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.538155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.739467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.914731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.346597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.545866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.739136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.929403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.101667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.546992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.710931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.979219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:33:59.989178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.174795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.403711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.623678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.824486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.999750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.436617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.631886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.824154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.013422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.186687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.632012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.794951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.065239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.078198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.260319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.493351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.710701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.908504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.309820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.525637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.717906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.909173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.097441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.270705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.715030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.880969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.149257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.166218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.345338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.583334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.794770image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.991523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.395841image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.613656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.802925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.992192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.182460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.353724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.800049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.965989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.232276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.253238image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.432357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.673353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.879790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.076542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.482402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.698675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.888944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.078212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.265479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.436743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.882068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.050007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.316295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.339257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.517376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.760399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.966290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.159561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.569421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.782695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.973963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.172233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.348497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.796824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.966086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.135027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.401314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.426276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.607397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.846419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.054310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.244581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.657442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.868715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.060983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.256252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.433516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.881842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.048782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.221046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.488334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.513296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.694417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.934439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.140332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.329600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.744461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.954734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.146002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.340270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.517535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:13.965861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.131801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.306065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.572353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.598315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.786873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.020458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.226351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.412618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.829481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.039753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.230021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.426290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.600554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.049880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.214820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.390084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.655371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.684334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.877409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.106477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.310370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.497637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:07.916501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.122772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.316040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.508308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.684573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.132899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.296838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.474103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.740391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.769354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:02.968864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.191496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.396389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.579656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.000519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.206791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.400059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.592327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.768592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.215918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.379857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.557121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.822409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:00.853373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.055447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.274515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.482409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.662674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.085538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.290809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.484077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.675345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.851611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.296936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.460875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.640140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:17.907428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:01.916678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:03.143781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:04.361478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:05.568429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:06.746694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:08.172558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:09.375829image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:10.569097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:11.760365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:12.934629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:14.379955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:15.543894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-04-09T19:34:16.724159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-04-09T19:34:21.345523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
(Total temperature at LPC outlet) (â—¦R)(Total temperature at HPC outlet) (â—¦R)(Total temperature at LPT outlet) (â—¦R)(Total pressure at HPC outlet) (psia)(Physical fan speed) (rpm)(Physical core speed) (rpm)(Static pressure at HPC outlet) (psia)(Ratio of fuel flow to Ps30) (pps/psia)(Corrected fan speed) (rpm)(Corrected core speed) (rpm)(Bypass Ratio)(Bleed Enthalpy)(HPT coolant bleed (lbm/s))(LPT coolant bleed (lbm/s))(Total pressure in bypass-duct) (psia)
(Total temperature at LPC outlet) (â—¦R)1.0000.5760.693-0.6800.6500.1040.717-0.7060.649-0.0190.6510.605-0.640-0.6430.146
(Total temperature at HPC outlet) (â—¦R)0.5761.0000.651-0.6430.5920.1430.670-0.6600.5930.0290.6130.575-0.599-0.6100.125
(Total temperature at LPT outlet) (â—¦R)0.6930.6511.000-0.7740.7390.1070.812-0.8000.737-0.0350.7320.678-0.727-0.7200.170
(Total pressure at HPC outlet) (psia)-0.680-0.643-0.7741.000-0.755-0.056-0.8060.794-0.7540.086-0.726-0.6740.7160.7150.184
(Physical fan speed) (rpm)0.6500.5920.739-0.7551.000-0.1790.777-0.7750.807-0.3260.6890.618-0.677-0.6770.180
(Physical core speed) (rpm)0.1040.1430.107-0.056-0.1791.0000.083-0.046-0.1820.8860.1150.154-0.111-0.1140.054
(Static pressure at HPC outlet) (psia)0.7170.6700.812-0.8060.7770.0831.000-0.8320.776-0.0660.7580.698-0.750-0.7500.199
(Ratio of fuel flow to Ps30) (pps/psia)-0.706-0.660-0.8000.794-0.775-0.046-0.8321.000-0.7770.101-0.745-0.6840.7340.7360.188
(Corrected fan speed) (rpm)0.6490.5930.737-0.7540.807-0.1820.776-0.7771.000-0.3280.6870.618-0.676-0.6770.186
(Corrected core speed) (rpm)-0.0190.029-0.0350.086-0.3260.886-0.0660.101-0.3281.000-0.0190.0320.0190.0200.082
(Bypass Ratio)0.6510.6130.732-0.7260.6890.1150.758-0.7450.687-0.0191.0000.643-0.682-0.6780.173
(Bleed Enthalpy)0.6050.5750.678-0.6740.6180.1540.698-0.6840.6180.0320.6431.000-0.626-0.6320.146
(HPT coolant bleed (lbm/s))-0.640-0.599-0.7270.716-0.677-0.111-0.7500.734-0.6760.019-0.682-0.6261.0000.6680.162
(LPT coolant bleed (lbm/s))-0.643-0.610-0.7200.715-0.677-0.114-0.7500.736-0.6770.020-0.678-0.6320.6681.0000.154
(Total pressure in bypass-duct) (psia)0.1460.1250.1700.1840.1800.0540.1990.1880.1860.0820.1730.1460.1620.1541.000

Missing values

2023-04-09T19:34:18.019453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-09T19:34:18.192492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

(Total temperature at LPC outlet) (â—¦R)(Total temperature at HPC outlet) (â—¦R)(Total temperature at LPT outlet) (â—¦R)(Total pressure in bypass-duct) (psia)(Total pressure at HPC outlet) (psia)(Physical fan speed) (rpm)(Physical core speed) (rpm)(Static pressure at HPC outlet) (psia)(Ratio of fuel flow to Ps30) (pps/psia)(Corrected fan speed) (rpm)(Corrected core speed) (rpm)(Bypass Ratio)(Bleed Enthalpy)(HPT coolant bleed (lbm/s))(LPT coolant bleed (lbm/s))
00.1837350.4068020.3097571.00.7262480.2424240.1097550.3690480.6332620.2058820.1996080.3639860.3333330.7131780.724662
10.2831330.4530190.3526331.00.6280190.2121210.1002420.3809520.7654580.2794120.1628130.4113120.3333330.6666670.731014
20.3433730.3695230.3705271.00.7101450.2727270.1400430.2500000.7953090.2205880.1717930.3574450.1666670.6279070.621375
30.3433730.2561590.3311951.00.7407410.3181820.1245180.1666670.8891260.2941180.1748890.1666030.3333330.5736430.662386
40.3493980.2574670.4046251.00.6682770.2424240.1499600.2559520.7462690.2352940.1747340.4020780.4166670.5891470.704502
50.2680720.2927840.2721131.00.7761670.1818180.1254150.1845240.6375270.2205880.1698320.3305120.2500000.6511630.652720
60.3825300.4639200.2619851.00.7230270.1818180.1678180.3035710.7739870.2205880.1670970.2789530.3333330.7441860.667219
70.4066270.2598650.3160031.00.6441220.1515150.0855690.2321430.8059700.2205880.1606460.3181990.2500000.6434110.574979
80.2740960.4347070.2118501.00.6183570.2272730.1109670.2619050.6609810.2500000.1328830.1843020.3333330.7054260.707539
90.1506020.4403750.3073941.00.6022540.2272730.1344790.1071430.6609810.2647060.1519250.3990000.4166670.6279070.794256
(Total temperature at LPC outlet) (â—¦R)(Total temperature at HPC outlet) (â—¦R)(Total temperature at LPT outlet) (â—¦R)(Total pressure in bypass-duct) (psia)(Total pressure at HPC outlet) (psia)(Physical fan speed) (rpm)(Physical core speed) (rpm)(Static pressure at HPC outlet) (psia)(Ratio of fuel flow to Ps30) (pps/psia)(Corrected fan speed) (rpm)(Corrected core speed) (rpm)(Bypass Ratio)(Bleed Enthalpy)(HPT coolant bleed (lbm/s))(LPT coolant bleed (lbm/s))
206210.7469880.8683240.7586091.00.3107890.5454550.2116580.7261900.2366740.5882350.2251010.7091190.8333330.1937980.314278
206220.6987950.6581640.6284601.00.2077290.4090910.1737410.7916670.4051170.4852940.2245850.6321660.7500000.3255810.252416
206230.5662650.6272070.7879811.00.3526570.5909090.2056900.7976190.3027720.4558820.2171530.8368600.7500000.2558140.177851
206240.7560240.5722690.7623231.00.3446050.5000000.2114330.7559520.1833690.5000000.2050260.7564450.5000000.1860470.328915
206250.6626510.6322210.8381161.00.2528180.5000000.2152020.8154760.2174840.5882350.2216950.8734130.5000000.0000000.411627
206260.6867470.5873120.7829171.00.2544280.4393940.1964910.7261900.1705760.5588240.1943440.6567910.7500000.2713180.109500
206270.7018070.7294530.8664751.00.1626410.5000000.1946510.7083330.2110870.5000000.1886680.7272030.5833330.1240310.366197
206280.6656630.6849790.7753211.00.1755230.5151520.1981960.7380950.2814500.5294120.2121480.9222780.8333330.2325580.053991
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